Minimum Time Ship Maneuvering using Neural Network and Nonlinear Model Predictive Compensator

本研究提出一种新的最短时间船舶操纵系统，该系统主要由两部分组成。一个是基于神经网络的最优解发生器，另一个是非线性模型预测补偿器。神经网络通过对典型控制条件下预先计算的最短时间解进行插值，生成真实的情况下的最优解。基于复杂的船舶非线性动力学模型（MMG模型），通过数值计算得到各种最短时间操纵的最优解，并由神经网络离线学习插值。此外，该非线性动力学模型，这是用于计算的最优解，被用来模拟船舶的未来航线在线。在计算出船舶未来航向的基础上，通过修正最小时间操纵控制输入来补偿由于某些干扰引起的预测控制误差。首先，简要回顾了最小时间操纵问题的求解方法和船舶动力学的数学模型。接下来，最小时间平行偏差机动问题及其解决方案作为一个例子，所提出的系统实现的可行性研究。然后，介绍了一种基于神经网络和非线性模型预测补偿器的最小时间机动控制系统。最后，以425总吨级训练船Shioji Maru为对象，进行了计算机仿真和在线实验，提出了一种新的实用船舶最短时间操纵控制系统。在最小时间偏差问题中，该系统在较短的计算时间内给出了近似解，并在真实的情况下具有良好的跟踪性能。此外，实际的海上试验证明了所提出的系统的有效性。

In this research, a new minimum-time ship maneuvering system is developed.The proposed system is mainly composed of two parts. The one is a neural network based optimal solution generator and another is a nonlinear model predictive compensator. The neural network generates the optimal solution for real situation by interpolating pre-computed minimum-time solutions for typical control conditions. The optimal solutions for the various minimum time maneuvering are numerically computed based on the sophisticated nonlinear dynamical model of the ship (MMG model) and are learned off-line by the neural network for interpolation. Moreover, the same nonlinear dynamical model, which is used for the computation of the optimal solutions, is used to simulate the ship's future course on-line. Based on the computed ship's future course, the predictive control error caused by some disturbances is compensated by modifying the control input for minimum time maneuvering.First, the solving technique of the minimum-time maneuvering problems and the mathematical model of the ship's dynamics are briefly reviewed. Next, the minimum-time parallel deviation maneuvering problem and its solutions are introduced as an example of the feasible study realized by proposed system. Then, a minimum-time maneuvering system with neural network and nonlinear model predictive compensator is introduced. Finally, computer simulations and on-line experiments are carried out for a training ship Shioji Maru (425 gross tonnage).This research presented a new practical ship's minimum-time maneuvering system with neural network and nonlinear model predictive compensator. In the minimum-time deviation problems, the system gives approximate solutions in a short computing time and good tracking performance in real situations. Moreover, the actual sea trials demonstrate the effectiveness of the proposed system.